As technology and science constantly evolve, so does the quantity and quality of data. To effectively collect and categorize data there has to be a new and updated strategy to do so. Otherwise, efficiency drops and a lot of unused data goes to waste. A successful data strategy is a great complement to a strong business strategy. The more data You know, the better decisions You can make.
Building Blocks of Data Strategy
Data strategy is something that can serve as a plan or a guide in terms of effectively collecting, storing, managing, using and sharing data.
Every company has its policies on data distribution and handling. Most of us have seen how military institutions use “Classified” folders in movies. That is actually, a part of their data strategy. To ensure that certain sensitive documents do not fall into the hands of competitors. The same way any company would have their data strategies in place to govern which data can be shared publicly and which could not.
According to the US-based analytics software company called SAS, a successful data strategy has 5 main parts or core components. A brief description of all five can be found below.
To ensure whether the data collected is relevant to the processes of the company, there has to be a system to identify the data. The data needs a definition. In a way that all parties involved in the data strategy can understand if the data they collected is necessary for their studies and analytical processes.
Once data is obtained, there has to be a way how and where to store it. Before modern computers, there were huge vaults of document folders that contained data stored in them. Nowadays these storage means are servers with encryptions, USB flash drives, cd’s, to name a few. Captured data has to be stored in a way so that involved parties can have access to it whenever needed.
The way how data is packaged so it can be shared and reused is essential to ensure a good data strategy. There have to be certain rules, guidelines on how data is provided to parties that need to access it. If a company does not have strict guidelines on how the data can be used after, then the data can easily fall into the hands of competitors. The original owners could not do anything about it unless there are some established rules.
Large volumes of data are obtained in bulk. Meaning, that data is a huge chunk of information that needs to be processed for people to comprehend it. Similarly, as writing an article. Writers use paragraphs, headlines, punctuation to give text meaning. So it can be read without misinterpretation. Same thing with data. It has to be processed and formatted in a way that all the users can grasp what lies within
To govern over data means to have sets of principles when it comes to the quality of the data. This means to have certain qualifiers which have to be there for the data to be accurate for corporate use. Security details of the data also are another thing when it comes to data governance. Otherwise, who can say that the data used by a company is not taken from another owner?
Future Of Data Strategy
The volume of data available increases every second. Similarly, to the population growth on Earth. The more people this planet has, the more potential businesses arise. With those businesses, more and more data is coming around. To keep up with the increasing numbers, new data strategies have to be thought out, or the older ones renewed.
An interesting study by the Financial Conduct Authority (FCA) has been made. By reviewing the methods used in their first data strategy, published in 2013. The first data strategy of FCA was made to serve as a guideline for them as data regulators. Now, they have set new goals for 2020 and the upcoming five years with their new data strategy and the way how it is going to be implemented. It is worth looking at some of the aspects they have planned to go through.
- Making it a priority to increase resources available for data science. To grow and make their capabilities in the field much more impactful
- Employing new tools, such as web scraping, network analytics for use in a wide range of scenarios
- Improving the way on how to increase the quality of data they receive and how it is being received
- Looking forward to new technologies that are being made and how they are detecting financial crime
These are a couple of things FCA has been planning to do in upcoming years to strengthen the integrity of data science and security around it. Theories have to be put to practice or else they will be theories forever.
A bold move from FCA, that is for sure, but then again – the biggest risk in life is not taking one.
data flow abstract -DepositPhotos